Geographical variation of Crohn's disease residual incidence in the Province of Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: Crohn's disease (CD) is clinically expressed as a chronic affection of the gastrointestinal tract currently known to have a multifactorial etiology involving a complex pathophysiological host response modulated by genetic susceptibilities, demographic determinants and environmental factors. With more than 20 cases per 100,000 person-years, the province of Quebec, Canada is among regions of the world with highest reported occurrence of CD in relation to other places where comparable estimates are available. This ecological study was designed to provide a medium-scale spatial exploration of CD incidence after accounting for the influence of known population and regional determinants. Health records of consulting patients in southern Quebec were compiled from 1995 to 2000 and used to estimate age and sex standardized rates per health area (n = 156). Various statistical models taking into account the regional effect of Jewish ethnicity, aboriginal ancestry, material deprivation, prescription for oral contraceptives, reportable enteric infection incidence, smoking as well as latitude and longitude locations were fitted. RESULTS: The final regression model presented a coefficient of determination of 22.8% and there was evidence of an eastern trend in the residual incidence (p = 0.018). Overall, the smoothed residual incidence presented a heterogeneous spatial pattern with evidence of patches (multiple health areas) of high, low and contrasting values. Health areas with most extreme incidence residuals where also distributed over the whole province including one area in the metropolitan area of Montreal and others in surrounding areas. CONCLUSIONS: These findings suggest that known populational and regional factors derived through census information only explain a limited fraction of the geographical variation of CD incidence and lead to speculate that the effects of these factors may be incompletely captured (imperfect construction of proxy variables) or that other important factors remain unmeasured. In this view, markers of genetic profiles of homogeneous sub-populations, and other factors linked to agroenvironmental microbial exposure should be further investigated. Once accounting for known factors, it would also be worth comparing adjacent geographical areas demonstrating abrupt changes in residual incidence rates to further explore effect linked to regional factors from those resulting from various reporting systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".